Effectiveness of Forensic Tools in Extracting Web Browser Artifacts
Comprehensive Multi-Browser Analysis
Scenario-Based Experimental Design
Multi-Tool Evaluation
Evidence of Residual Artifacts in Privacy Modes
Predicting Online Delivery Adoption During COVID-19: A Machine Learning Approach
The significant research contribution of this study lies in its comprehensive analysis of the sociodemographic and behavioral factors influencing online delivery adoption during the COVID-19 pandemic, utilizing advanced machine learning techniques. By employing a structured preprocessing pipeline and comparing multiple models, the study identified LightGBM as the most effective classifier, achieving an accuracy of 97% and an F1-score of 0.96 for both classes, which underscores its capability to capture complex patterns in consumer behavior. The findings revealed that younger age, higher household income, and education level were the most influential predictors of increased delivery use, providing valuable insights for transportation planners and policymakers. This research not only enhances understanding of consumer behavior during a critical period but also offers a framework for future studies on urban logistics and service adaptation in response to evolving consumer needs.
LiteFakeNet: Efficient Deepfake Image Detection with Depthwise Separable Convolutions
The significant research contribution of this study is the development of LiteFakeNet, a novel lightweight convolutional neural network (CNN) designed for efficient deepfake image detection, achieving an accuracy of 95%, precision of 96%, and recall of 94% on the CIFAKE dataset, which consists of 120,000 images. LiteFakeNet utilizes depthwise separable convolutions to balance high performance with low computational and energy costs, featuring less than 83,000 parameters and only 0.16 million FLOPs, making it more efficient than existing models like MobileNet and ResNet50. This model not only addresses the urgent need for effective deepfake detection but also aligns with the principles of Industry 5.0 by promoting human-AI collaboration and sustainable technology.
StegoVision: Enhanced Video Steganography via Prewitt Edge Mapping and 3-XOR Secured LSB
The significant research contribution of this study is the development of an automated two-tier data concealment technique for video steganography that integrates Advanced Encryption Standard (AES) encryption with a robust steganographic method, enhancing data security and imperceptibility. This approach utilizes a 128-bit AES key for encrypting sensitive information, which is then embedded into video frames using a combination of the Least Significant Bit (LSB) method and a Prewitt pixel selection technique, achieving superior data invisibility while maintaining frame quality. The proposed model also incorporates a Fisher-Yates randomization method for frame selection, which increases the resilience and payload capacity of the steganographic process, and demonstrates improved performance metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) compared to existing techniques.
Exploring the Research Landscape of DCT-Based Video Steganography: A Systematic Review
The systematic literature review highlights significant advancements in DCT-based video steganography, particularly through hybrid techniques that combine DCT with other methods like DWT and error correction codes (ECC) to enhance imperceptibility and robustness. Notable contributions include the use of deep learning models, such as CNNs, to maintain visual quality while embedding data, and the development of coverless techniques that do not alter the original video content. The review also emphasizes the importance of performance metrics like PSNR and MSE for evaluating the effectiveness of these techniques.
Hyperparameter-Tuned Logistic Regression for Early-Stage Breast Cancer Prediction using Explainable AI
• Design of HTLR model using optimized logistic regression applied to early stage breast cancer prediction.
• Integration of SHAP for model interpretability, supporting clinicians in extracting key features affecting malignant and benign predictions.
• Comparative analysis with baseline state-of-the-art models and existing systems, demonstrating that the proposed HTLR achieved 99.12% accuracy while providing transparent and clinically meaningful explanations.
Structured Prompting and Multi-Agent Reasoning for Open-Source Financial Sentiment Analysis
• A novel structured prompting methodology that provides context-aware, declarative instructions to coordinate multi-agent collaboration, and an output aggregation mechanism that assigns greater weight to more confident
agent responses, improving reliability and interpretability.
• A manually annotated, domain-specific dataset designed to support uncontaminated, realistic performance evaluation.
• A resource-efficient system combining LLaMA 3.1 8B for specialized subtasks and LLaMA 3 70B for synthesis, balancing scalability and performance.
Analytical Framework for a Green Room: An Integrated Passive Cooling Approach for Sustainable and Climate Resilient Buildings
This research introduces the Green Room framework, the first closed-form model integrating four passive cooling strategies. By quantifying synergistic effects and embedding environmental drivers, it delivers a retrofit-ready, simulation-free solution capable of 41–44 °C cooling, establishing a scalable pathway toward sustainable, climate-resilient, and energy-efficient building design.
Accelerated Skin Prick Test (aSPT): A Low-Cost, AI-Guided Allergy Diagnostic System Using Microneedles and Image-Based Severity Scoring
Introduces a low-cost allergy diagnostic integrating a dual-layer microneedle patch with offline AI image analysis, enabling painless, rapid, and accessible testing under $4 per test with inference in <1s per allergen site